Project Summary/Abstract: Post-operative pain is a major contributor to the current opioid epidemic. Lack of objective measures proven capable of quantifying risk of post-operative pain both reliably and accurately is a major obstacle to reducing the consequences of post-operative pain states. There is currently a need for discovery of novel, objective measures capable of personalizing pain care by enhancing medical precision in prevention and treatment of post-operative pain. Evidence from animal models suggests neuroimmune signaling between brain mu-opioid receptors and imbalanced pro- and anti-nociceptive plasma IL-1 family cytokines enhance risk of persistence of post-operative pain states. However, limited validation of this evidence in-vivo in humans has burdened clinical translation. The overarching goal of this project is to discover and validate a novel bio-signature of the human pain experience (based on underlying IL-1 family cytokine activity and associated brain endogenous opioid function) that is readily quantifiable and clinically translatable to prevention and treatment of post-operative pain states. The central translational nature of the proposed research is highly innovative. During an experimental, standardized, nociceptive pain challenge, we will induce a moderate level of sustained pain in n=70 healthy humans awaiting cosmetic body contouring surgery while simultaneously quantifying mu-opioid receptor activity in the brain via 11C-carfentanil PET neuroimaging. Concentration of a wide range of pro- and anti-nociceptive IL-1 family cytokines will be quantified from plasma obtained prior to the challenge. Using an iterative, software driven process involving hierarchical linear regression of IL-1 family cytokines (independent predictor variables), we will discover (R61 Discovery phase) a bio- signature that will predict extent of pain (intensity and threshold) induced (and endogenous opioid neurotransmitter release) during the nociceptive pain challenge. Subsequently, we will show the bio-signature predicts (accurately, reliably, and with broad dynamic range) the onset (end extent) of post-operative pain in an expanded, more clinically heterogeneous sample (R33 validation phase). Specific Aims will show that the novel bio-signature will predict 1) experimentally induced pain (and underlying endogenous opioid release) during an experimental nociceptive pain challenge, 2) post-operative pain states with accuracy >75%, accounting for a wide range of variance in the human pain experience, and 3) post-operative pain states in an expanded clinically enriched sample.